How to Map Intent Data to the Buyer's Journey (With the LeadOcean API)

Buyer's journey intent data is a signal tagged with the stage it points to: awareness, consideration or decision. The tag decides what you send. A topic spike is not a pricing-page visit, and one email should not treat them the same.

LeadOcean has no intent feed. It holds the people and company data that signals attach to, plus filters that work as proxies. This guide shows both halves.

TL;DR

• Sort every signal you own into one of three stages, then attach a LeadOcean query to each stage so you know who to contact.
• It takes about 25 minutes and spends about 110 records. Counting is free.
• Third-party intent is account-level and fuzzy. Your own site and product signals are the strong ones. Use filters to find the people behind them.

Prerequisites

• A LeadOcean API key in $LEADOCEAN_API_KEY. The Free plan has 1,000 records for life, no card, 5 requests a second.
• curl and jq.
• A list of signals you already collect: site visits, pricing views, demo requests, a third-party topic feed, or none yet.
• Optional: the hosted MCP server at https://api.leadocean.io/mcp (OAuth sign-in, no key pasted) to run the counts from an assistant.

Step 1: Tag each signal with a stage

Put each signal in the stage it implies, then write down the account-level fact and the person-level fact you would need. This file is the whole model.

{
  "awareness": {
    "signals": ["topic surge on a third-party feed", "blog or docs visit", "new in role"],
    "need": "right company and a role that owns the problem",
    "leadocean": "technologyCategories + jobFunction + jobLevel"
  },
  "consideration": {
    "signals": ["compare or alternatives page", "webinar signup", "repeat visits from one domain"],
    "need": "the buying group at that domain",
    "leadocean": "domain + seniority, GET /v1/companies/{domain}/people"
  },
  "decision": {
    "signals": ["pricing page", "demo request", "trial or free signup"],
    "need": "one verified contact at the deciding level",
    "leadocean": "POST /v1/people/enrich + reveal_email"
  }
}

Third-party feeds fill awareness best. Decision signals almost always come from your own product and site.

Step 2: Size each stage for free

A count call spends nothing and tells you if the list is worth building. Send count=true as a query parameter with limit=1.

curl -s -X POST "https://api.leadocean.io/v1/people/search?count=true&limit=1" \
  -H "x-api-key: $LEADOCEAN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "technologyCategories": ["Marketing automation"],
    "jobFunction": ["Advertising & Marketing"],
    "jobLevel": ["VP", "C-Team"],
    "country": ["US"]
  }' | jq '.meta'

The technologyCategories filter reads technology detected on company websites, for about 7.8M crawled companies. A company running a marketing automation tool is a stage-one proxy: it has the problem you sell against.

Funding is a second proxy. minLastFundingYear narrows a search, but do not count with it. It applies only when the matching companies number 50,000 or fewer, and counts that use it can come back wrong.

Step 3: Pull the awareness and consideration lists

Page through the search once the count looks right. The body is flat JSON, filters are arrays, limit runs 1 to 100.

curl -s -X POST "https://api.leadocean.io/v1/people/search" \
  -H "x-api-key: $LEADOCEAN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "technologyCategories": ["Marketing automation"],
    "jobFunction": ["Advertising & Marketing"],
    "jobLevel": ["VP", "C-Team"],
    "country": ["US"],
    "emailStatus": ["verified", "catch_all_valid"],
    "limit": 100
  }' | jq '{total: .meta.total, next: .meta.nextCursor}'

Pass meta.nextCursor as cursor for the next page. One search stops at 10,000 rows. When meta.depthCapped is true, narrow the filters instead of walking further.

For consideration, a visitor domain from your analytics is the signal. Pull the people at that account.

curl -s "https://api.leadocean.io/v1/companies/acme.com/people?limit=100" \
  -H "x-api-key: $LEADOCEAN_API_KEY" | jq '.meta'

Step 4: Enrich the decision-stage contact

A demo request gives you an email or a LinkedIn URL. Enrich takes exactly one of linkedin_url, email, phone or person_id. Set reveal_email to get the address back.

curl -s -X POST "https://api.leadocean.io/v1/people/enrich" \
  -H "x-api-key: $LEADOCEAN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"email": "jane.doe@acme.com", "reveal_email": true}'

Enrich also returns career history in resume_data: past roles, education and certifications. The current role sits in contact_data.contact_current_experiences[0] with its start_date, so you can see how long the contact has held it. An enrichment that finds nobody is a 404 and costs nothing.

What you get

This is the Step 2 query run through the MCP count tool on 2026-10-21, trimmed to the fields that matter. It cost 0 records.

{
  "total": 2898,
  "totalAll": 6810,
  "distinctCompanies": 1917,
  "credits": 0,
  "defaults": "Counted only addresses worth sending to (emailStatus: verified or catch_all_valid or catch_all)"
}

Read it as: 2,898 US marketing VPs and C-level people, at companies with detected marketing automation, with a mailable email, spread over 1,917 companies. The same filters without the email condition match 6,810 people.

Swap VP and C-Team for Director and Manager and the same run returned 23,500 mailable people across 8,040 companies. The senior list is small enough to hand-work. The manager list needs scoring first.

Response shape from the API reference, placeholder values, abridged:

{
  "data": [
    { "profile_data": { "profile_full_name": "Jane Doe" },
      "contact_data": {
        "contact_current_experiences": [
          { "job_title": "VP Marketing", "company_name": "Acme", "company_domain": "acme.com" }
        ],
        "has_email": true,
        "email_status": "verified"
      },
      "meta": { "source_ids": { "person_id": "<person_id>" } } }
  ],
  "meta": { "total": 2898, "nextCursor": "opaque-string", "depthCapped": false }
}

On an enriched record, past roles sit in resume_data.experiences[] with job_title, company_name, start_date, end_date and job_seniority.job_level. Past employers carry no company_id yet, so match them by name. Search rows carry no email address, only the has_email and email_status flags.

Troubleshooting

FAQ